Weapon Detection in X-ray Image of Baggages

dc.contributor.authorKundilokovit, Piyapat
dc.contributor.authorThaweechoklertchaikul, Rimthaweep
dc.contributor.authorAnuntachai, Anuntapat
dc.date.accessioned2026-08-06T10:43:16Z
dc.date.available2026-08-06T10:43:16Z
dc.date.issued2024-01-01
dc.description.abstractDue to the daily commutes of people by MRT trains, following the shooting incident at Paragon, the MRT system has implemented bag searches before entering the stations to look for concealed or hidden weapons. These searches are conducted manually, which sometimes may not be thorough enough and can take a significant amount of time. Especially during peak hours when many people are using the MRT, it is possible for some individuals to pass through the station without being searched. Such actions can render the security measures ineffective. Therefore, this paper proposes a study to find ways to address these issues. From the study and comparison of object detection processes for risky items, such as sharp objects or guns, in X-ray images of luggage, it was found that models such as CNN, RCNN, Detectron, RetinaNet, and Yolo achieved excellent results in object detection and recognition. The organizers plan to apply object detection techniques and improve the existing methods for detecting objects in X-ray images to be more efficient and accurate, capable of identifying a variety of risky items.
dc.identifier.citationConference Proceeding 23rd International Symposium on Communications and Information Technologies Iscit 2024, 88-93, 2024
dc.identifier.doi10.1109/ISCIT63075.2024.10793527
dc.identifier.other2-s2.0-85216525709
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14888
dc.sourceConference Proceeding 23rd International Symposium on Communications and Information Technologies Iscit 2024
dc.subjectImage Processing
dc.subjectNeural Networks
dc.subjectObject Detection
dc.subjectYOLO
dc.titleWeapon Detection in X-ray Image of Baggages
dc.typeConference Paper

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